Spring Hill (NNP-I 1000): Intel's Data Center Inference Chipinside-BigData.com
Today at Hot Chips 2019, Intel revealed new details of upcoming high-performance AI accelerators: Intel Nervana neural network processors, with the NNP-T for training and the NNP-I for inference. Intel engineers also presented technical details on hybrid chip packaging technology, Intel Optane DC persistent memory and chiplet technology for optical I/O.
"To get to a future state of ‘AI everywhere,’ we’ll need to address the crush of data being generated and ensure enterprises are empowered to make efficient use of their data, processing it where it’s collected when it makes sense and making smarter use of their upstream resources," said Naveen Rao, Intel vice president and GM, Artificial Intelligence Products Group. "Data centers and the cloud need to have access to performant and scalable general purpose computing and specialized acceleration for complex AI applications. In this future vision of AI everywhere, a holistic approach is needed—from hardware to software to applications.”
Learn more: https://www.intel.ai/accelerating-for-ai/?elq_cid=1192980
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Spring Hill (NNP-I 1000): Intel's Data Center Inference Chipinside-BigData.com
Today at Hot Chips 2019, Intel revealed new details of upcoming high-performance AI accelerators: Intel Nervana neural network processors, with the NNP-T for training and the NNP-I for inference. Intel engineers also presented technical details on hybrid chip packaging technology, Intel Optane DC persistent memory and chiplet technology for optical I/O.
"To get to a future state of ‘AI everywhere,’ we’ll need to address the crush of data being generated and ensure enterprises are empowered to make efficient use of their data, processing it where it’s collected when it makes sense and making smarter use of their upstream resources," said Naveen Rao, Intel vice president and GM, Artificial Intelligence Products Group. "Data centers and the cloud need to have access to performant and scalable general purpose computing and specialized acceleration for complex AI applications. In this future vision of AI everywhere, a holistic approach is needed—from hardware to software to applications.”
Learn more: https://www.intel.ai/accelerating-for-ai/?elq_cid=1192980
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
FMX 2017: Extending Unreal Engine 4 with Plug-ins (Master Class)Gerke Max Preussner
Overview on creating code projects and plug-ins, and introduction on how to add new asset types to UE4 and customize their look & feel via asset actions and custom asset editors. The corresponding source code is available at https://headcrash.industries/vault/presentations/fmx/
FMX 2017: Extending Unreal Engine 4 with Plug-ins (Master Class)Gerke Max Preussner
Overview on creating code projects and plug-ins, and introduction on how to add new asset types to UE4 and customize their look & feel via asset actions and custom asset editors. The corresponding source code is available at https://headcrash.industries/vault/presentations/fmx/
Visibility in Manufacturing: The Path to Industry 4.0Ubisense
Industry 4.0 is the next Industrial Revolution. It involves integrating data from many kinds of sensors to gain the necessary visibility to predict issues and self-diagnose as problems occur. To some manufacturers, the coming age of cyber-physical systems is the inevitable next step. For most, however, these concepts feel quite distant from today’s reality where many plants have yet to adopt the automation technologies of Industry 3.0, and are decades away from mass adoption of Industry 4.0.
While today Industry 4.0 is an aspiration, every company can benefit from greater visibility throughout the production process. This slide deck explores how the manufacturing plant is evolving from recording the past to controlling the present to predicting the future.
What You'll Learn From this Presentation:
-Where your company ranks among the four Industrial Revolutions
-How to get to the next stage
-How increased visibility can have a positive and resounding impact throughout the manufacturing process
-What it takes to embrace Industry 4.0
2. 網際網路演化進程
2
Wave 1 : WWW
~350M PCs annually
Wave 2 : Mobile/Cloud
2.3B annually
Wave 3 : Internet of Things
50B by 2020*
Connecting PCs Connecting People Connecting Everything
Source : Harvard University
3. 3
Internet of Things (IoT) 是實體物件(Things)所形成的網路，
物件包含電子晶片, 軟體, 感應器與網路介面，IoT物件與
工廠, 或營運商或其他IoT物件透過資料交換服務而產生價
值。IoT物件即是一台電腦系統，它具有唯一可識別性，
與現有網際網路相容。
Source : Internet of Things, Wikipedia
“在物聯網的世界，即便是牛都將連接到網路”
Source : The Economist 2010
4. IoT預期數量與市場規模
4
Category 2014 2015 2016 2020
Consumer 2,277 3,023 4,024 13,509
Business: Cross-
Industry
632 815 1,092 4,408
Business: Vertical-
Specific
898 1,065 1,276 2,880
Grand Total 3,807 4,902 6,392 20,797
Category 2014 2015 2016 2020
Consumer 257 416 546 1,534
Business: Cross-
Industry
115 155 201 566
Business:
Vertical-Specific
567 612 667 911
Grand Total 939 1,183 1,414 3,010
Internet of Things Units Installed Base by Category (Millions of Units)
Internet of Things Endpoint Spending by Category (Billions of Dollars)
Source : Gartner, Nov 2015
7. Example of Model-based Technique : Kalman Filter
7
Probabilistic Models: In sensor data cleaning, inferring sensor values is perhaps the most import
task, since systems can then detect and clean dirty sensor values by comparing raw sensor values
with the corresponding inferred sensor values.
The Kalman filter is perhaps on of the most common probabilistic models to compute inferred
values corresponding to raw sensor values.
21. 工業4.0 論述分析
21
滑坡謬誤(Slippery slope fallacy)
使用連串的因果推論，誇大了每個環節的因果強度，而得到不
合理的結論。
預測創新科技往往高估它們的價值。依據科技預測產生的商業
決策將導致非常高的風險。
David Wheeler. “Toward more realistic forecasts for high-tech products”
網際網路對經濟的影響遠不及傳真機
Paul Krugman. American economist
最終結論(工業4.0)依賴若干未經驗證的情境上(IoT, Big Data, 3D Printing..)
22. 商業模式與營運模式不匹配
22
Total Product Life Cycle
and Birth to Death Expenditures
1000 Ideas 100 Trials 10 Products 1 Success
+
Sales Curve
Patent Filed
Licensees Profits
Sought
Idea
0 +
Reduction
to Practice
Shutdown Costs
Total Expenditure/Profit Curve
Pilot Plant
M arketing Starts
新創公司
Startup
高失敗率
創新想法
與變更Pivot
工業/工廠
Manufacturer
低失敗率
成熟技術與固定製程
IoT / Big Data
商業模式 營運模式 績效統計
商業模式 營運模式 績效統計
